Modern communication networks, such as mobile phone networks and the Internet, and the plethora of devices that provide access to services that they provide have not only made people intensely aware of each other, but have inundated them with a surfeit of information and options for satisfying any from the simplest to the most complex needs and desires. Whereas in the not too distant past, information available to an individual was relatively sparse and generally expensive in time and/or resources to acquire, today, information—wanted and unwanted—is relatively inexpensive. All too often, the information is overwhelmingly abundant and diluted with irrelevant information.
For example, today a person interested in choosing a movie may receive for review via the Internet and mobile phone or cable networks, a bewildering number of recommendations for many tens, if not hundreds, of movies. Each movie may be accompanied with options for viewing at home, at conventional movie theaters, on a desktop computer, laptop, notebook, and/or on a smartphone. A person in transit, on foot or in a vehicle, using a laptop or smartphone, can easily request suggestions for a choice of coffee shops or restaurants to patronize, and may receive a list of recommended suggestions of confusing length. Whereas, the cost of acquiring information appears to have plummeted, the task of managing its copiousness and various options to determine its relevance has become an increasingly complex and expensive task.
Various recommender systems and algorithms have been developed to attempt to deal with the challenges and opportunities that the abundance of information has generated, and to automatically focus and filter information to match a business's, organization's or person's, interests and needs. The systems and algorithms typically acquire and process explicit and/or implicit data acquired for people to determine characteristics of the people and their consumer histories that may be used to infer their preferences for various information, products, and/or activities, generically referred to as “items”.
Explicit data comprises information that a person consciously provides responsive to explicit requests for the information. Implicit data comprises data acquired responsive to observations of a person's behavior that are not consciously generated in response to an explicit request for information. The characteristics and/or their representations are used to configure and filter information recommended to a person to improve relevance of the recommended information and reduce an amount of irrelevant data that accompanies and dilutes the information.
Common recommender algorithms for automatically inferring and recommending items that a person might be interested in are algorithms referred to as “collaborative filtering” and “content-based filtering” algorithms. A recommender system using a collaborative filtering algorithm recommends an item to an individual if persons sharing a commonality of preferences with the individual have exhibited a preference for the item. For example, if the individual has shown a preference for item “A” in the past, and persons in the database who have shown preference for item A have also shown preference for an item “B”, then item B may preferentially be recommended to the individual. In accordance with a content-based filtering algorithm, a recommender system recommends an item to an individual if the item shares a similarity with items previously preferred by the individual. For example, if the individual has shown a preference for action movies, the algorithm may preferentially recommend an action movie to the individual.